Source code for evalml.pipelines.components.estimators.classifiers.catboost_classifier

import numpy as np
import pandas as pd
from sklearn.preprocessing import LabelEncoder
from skopt.space import Integer, Real

from evalml.model_family import ModelFamily
from evalml.pipelines.components.estimators import Estimator
from evalml.problem_types import ProblemTypes
from evalml.utils import SEED_BOUNDS, get_random_seed, import_or_raise


[docs]class CatBoostClassifier(Estimator): """ CatBoost Classifier, a classifier that uses gradient-boosting on decision trees. CatBoost is an open-source library and natively supports categorical features. For more information, check out https://catboost.ai/ """ name = "CatBoost Classifier" hyperparameter_ranges = { "n_estimators": Integer(10, 1000), "eta": Real(0, 1), "max_depth": Integer(1, 16), } model_family = ModelFamily.CATBOOST supported_problem_types = [ProblemTypes.BINARY, ProblemTypes.MULTICLASS] SEED_MIN = 0 SEED_MAX = SEED_BOUNDS.max_bound
[docs] def __init__(self, n_estimators=1000, eta=0.03, max_depth=6, bootstrap_type=None, random_state=0): random_seed = get_random_seed(random_state, self.SEED_MIN, self.SEED_MAX) parameters = {"n_estimators": n_estimators, "eta": eta, "max_depth": max_depth} if bootstrap_type is not None: parameters['bootstrap_type'] = bootstrap_type cb_error_msg = "catboost is not installed. Please install using `pip install catboost.`" catboost = import_or_raise("catboost", error_msg=cb_error_msg) self._label_encoder = None cb_classifier = catboost.CatBoostClassifier(**parameters, random_seed=random_seed, silent=True, allow_writing_files=False) super().__init__(parameters=parameters, component_obj=cb_classifier, random_state=random_state)
[docs] def fit(self, X, y=None): cat_cols = X.select_dtypes(['category', 'object']) # For binary classification, catboost expects numeric values, so encoding before. if y.nunique() <= 2: self._label_encoder = LabelEncoder() y = pd.Series(self._label_encoder.fit_transform(y)) model = self._component_obj.fit(X, y, silent=True, cat_features=cat_cols) return model
[docs] def predict(self, X): predictions = self._component_obj.predict(X) if self._label_encoder: return self._label_encoder.inverse_transform(predictions.astype(np.int64)) return predictions
@property def feature_importances(self): return self._component_obj.get_feature_importance()